Making Sense at Scale with the Berkeley Data Analytics Stack

Transcription

1 Making Sense at Scale with the Berkeley Data Analytics Stack Michael Franklin February 3, 2015 WSDM 2015 Shanghai UC BERKELEY

2 Agenda A Little Bit on Big Data AMPLab Overview BDAS Philosophy: Unification not Specialization Spark, GraphX, and other BDAS components Wrap Up: Thoughts on Big Data Software

3 Sources Driving Big Data It s All Happening On- line Every: Click Ad impression Billing event Fast Forward, pause, Friend Request Transaction Network message Fault User Generated (Web & Mobile).. Internet of Things / M2M Scientific Computing

4 Big Data A Bad Definition Data sets, typically consisting of billions or trillions of records, that are so vast and complex that they require new and powerful computational resources to process. - Dictionary.com <Big Data as a Problem!>

5 Big Data as a Resource For a long time, we thought that Tamoxifen was roughly 80% effective for breast cancer patients. But now we know much more: we know that it s 100% effective in 70% to 80% of the patients, and ineffective in the rest. - Tim O Reilly et al. How Data Science is Transforming Health Care With enough of the right data you can determine precisely who the treatment will work for. Thus, even a 1% effective drug could save lives

6 A Big Data Pattern 750,000+ downloads 6

7 AMPLab: Integrating 3 Resources Algorithms Machine Learning, Statistical Methods Prediction, Business Intelligence Machines Clusters and Clouds Warehouse Scale Computing People Crowdsourcing, Human Computation Data Scientists, Analysts

8 AMPLab Overview UC BERKELEY Berkeley s AMPLab has already left an indelible mark on world of information technology, and even the web. But we haven t yet experienced the full impact of the group Not even close. Derrick Harris, GigaOM, Aug 2, Students, Postdocs, Faculty and Staff from: Databases, Machine Learning, Systems, Security, and Networking Franklin Jordan Stoica Culler Goldberg Joseph Katz Patterson Recht Shenker 50/50 Split: Industry Sponsors and Govt White House Big Data Program: NSF CISE Expeditions in Computing and Darpa XData Fixed Timeline (ends Dec 2016); Collaborative Working Space See Dave Patterson How to Build a Bad Research Center, CACM March, 2014

9 A Nexus of Industrial Engagement Open Source Software Industrial-Strength Twice-yearly 3-day offsite retreats; AMPCamp training

10 Open Source Community Building MeetUp on (Aug 6, 2013) Spark Summit SF (June 30, 2014)

11 Big Data Ecosystem Evolution Pregel Giraph MapReduce General batch" processing Dremel Impala Storm Drill S4 Tez GraphLab Specialized systems (iterative, interactive and" streaming apps)

12 AMPLab Unification Philosophy Don t specialize MapReduce Generalize it! Two additions to Hadoop MR can enable all the models shown earlier! 1. General Task DAGs 2. Data Sharing For Users: Fewer Systems to Use Less Data Movement SparkSQL Streaming GraphX Spark MLbase

13 Berkeley Data Analytics Stack (Apache and BSD open source) In-house Apps Access and Interfaces Processing Engine Storage Resource Virtualization

14 It s only September but it s already clear that 2014 will be the year of Apache Spark -- Datanami, 9/15/14 In-Memory Dataflow System Developed in AMPLab and its predecessor the RADLab Alternative to Hadoop MapReduce x speedup for ML and interactive queries Central component of the BDAS Stack Graduated to Apache Foundation -> Apache Spark M. Zaharia, M. Choudhury, M. Franklin, I. Stoica, S. Shenker, Spark: Cluster Computing with Working Sets, USENIX HotCloud, 2010.

15 Apache Spark Contributors: contributors to current release

16 Apache Spark: Compared to Other Projects MapReduce YARN HDFS Storm Spark Activity in past 6 months 0 MapReduce YARN HDFS Storm Spark 2-3x Commits more activity than: Hadoop, Storm, MongoDB, NumPy, D3, Julia, Lines of Code Changed

17 Iteration in Map-Reduce Initial Model Map Reduce Learned Model w (0) w (1) Training Data w (2) w (3)

18 Cost of Iteration in Map-Reduce Initial Model w (0) Map Reduce Learned Model w (1) Training Data Read 2 Repeatedly w (2) load same data w (3)

19 Cost of Iteration in Map-Reduce Initial Model Map Reduce Learned Model w (0) w (1) Training Data Redundantly save output between w (2) stages w (3)

20 Dataflow View Map Reduce Training Data (HDFS) Map Reduce Map Reduce

21 Memory Opt. Dataflow Training Data (HDFS) Cached Load Map Map Reduce Reduce Map Reduce

22 Memory Opt. Dataflow View Training Data (HDFS) Map Map Reduce Reduce Efficiently move data between stages Map Reduce Spark: faster than Hadoop MapReduce

23 Resilient Distributed Datasets (RDDs) API: coarse-grained transformations (map, group-by, join, sort, filter, sample, ) on immutable collections Resilient Distributed Datasets (RDDs)» Collections of objects that can be stored in memory or disk across a cluster» Built via parallel transformations (map, filter, )» Automatically rebuilt on failure Rich enough to capture many models:» Data flow models: MapReduce, Dryad, SQL,» Specialized models: Pregel, Hama, M. Zaharia, et al, Resilient Distributed Datasets: A fault-tolerant abstraction for in-memory cluster computing, NSDI 2012.

24 Abstraction: Dataflow Operators map filter groupby sort union join leftouterjoin rightouterjoin reduce count fold reducebykey groupbykey cogroup cross zip sample take first partitionby mapwith pipe save...

25 Fault Tolerance with RDDs RDDs track the series of transformations used to build them (their lineage)» Log one operation to apply to many elements» No cost if nothing fails Enables per-node recomputation of lost data messages = textfile(...).filter(_.contains( error )).map(_.split( \t )(2)) HadoopRDD path = hdfs:// FilteredRDD func = _.contains(...) MappedRDD func = _.split( )

26 A Unified System for SQL & ML Deep integration of SQL and Spark Both share the same set of workers and caches def logregress(points: RDD[Point]): Vector { var w = Vector(D, _ => 2 * rand.nextdouble - 1) for (i <- 1 to ITERATIONS) { val gradient = points.map { p => val denom = 1 + exp(-p.y * (w dot p.x)) (1 / denom - 1) * p.y * p.x }.reduce(_ + _) w -= gradient } w } val users = sql2rdd("select * FROM user u JOIN comment c ON c.uid=u.uid") Can move seamlessly between SQL and Machine Learning worlds val features = users.maprows { row => new Vector(extractFeature1(row.getInt("age")), extractfeature2(row.getstr("country")),...)} val trainedvector = logregress(features.cache()) R. Xin, J. Rosen, M. Zaharia, M. Franklin,S. Shenker, I. Stoica, Shark: SQL and Rich Analytics at Scale, SIGMOD 2013.

27 Spark Streaming Microbatch approach provides lower latency Additional operators provide windowed operations M. Zaharia, et al, Discretized Streams: Fault-Tollerant Streaming Computation at Scale, SOSP 2013.

28 Batch/Streaming Unification Batch and streaming codes virtually the same» Easy to develop and maintain consistency // count words from a file (batch) val file = sc.textfile("hdfs://.../pagecounts-*.gz") val words = file.flatmap(_.split(" ")) val wordcounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordcounts.print() // count words from a network stream, every 10s (streaming) val ssc = new StreamingContext(args(0), "NetCount", Seconds(10),..) val lines = ssc.sockettextstream("localhost, 3456) val words = lines.flatmap(_.split(" ")) val wordcounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordcounts.print() ssc.start()

29 Apache Spark v1.2 (12/14) Includes» Spark (core)» Spark Streaming» GraphX (alpha release)» MLlib» SparkSQL Query Processing Wide range of interfaces:» Python / interactive ipython» Scala / interactive scala shell» R / interactive R-shell» Java Now included in all major Hadoop Distributions

30 Graph Analytics Link Table Hyperlinks PageRank Top 20 Pages Title Link Title PR Raw Wikipedia < / > < / > < / > XML Top Communities Com. PR.. Discussion Table Editor Graph Community Detection User Community User Disc. User Com.

31 Separate Systems Tables Graphs

32 Dataflow Systems Separate Systems Graphs Table Row Row Row Result Row

33 Separate Systems 6. Before Dataflow Systems Graph Systems 7. After 8. After Table Row Dependency Graph Row Row Result Row

34 Difficult to Use Users must Learn, Deploy, and Manage multiple systems Leads to brittle and often complex interfaces 34

35 Efficiencies are Possible Hadoop 1340 Spark 354 GraphLab Runtime (in seconds, PageRank for 10 iterations) Live-Journal Graph Specialized Graph System can be faster than general MR computation

36 But Extensive data movement and duplication across the network and file system < / > < / > < / > XML HDFS HDFS HDFS HDFS Limited reuse internal data-structures across stages 36

37 GraphX Adding Graphs to the Mix Table View GraphX Unified Representation Graph View Tables and Graphs are composable views of the same physical data Each view has its own operators that exploit the semantics of the view to achieve efficient execution J. Gonzalez, R. Xin, A. Dave, D. Crankshaw, M. Franklin, I. Stoica, GraphX: Graph Processing in a Distributed Dataflow Framework OSDI Conf., Oct 2014

38 Representation Join Distributed Graphs Horizontally Partitioned Tables Vertex Programs Dataflow Operators Optimizations Advances in Graph Processing Systems Distributed Join Optimization Materialized View Maintenance

39 Property Graph Data Model Property Graph B C A D Vertex Property: User Profile Current PageRank Value Edge Property: F E Weights Timestamps

40 Encoding Property Graphs as Tables Property Graph B C A Vertex Cut F E Part. 1 D Part. 2 Vertex Table (RDD) Machine 1 Machine 2 A B C D E F Routing Table (RDD) A B C D E F Edge Table (RDD) A A B C A A E E B C C D E F D F

41 Graph Operators (Scala) class Graph [ V, E ] { def Graph(vertices: Table[ (Id, V) ], edges: Table[ (Id, Id, E) ]) // Table Views def vertices: Table[ (Id, V) ] def edges: Table[ (Id, Id, E) ] def triplets: Table [ ((Id, V), (Id, V), E) ] // Transformations def reverse: Graph[V, E] def subgraph(pv: (Id, V) => Boolean, pe: Edge[V,E] => Boolean): Graph[V,E] def mapv(m: (Id, V) => T ): Graph[T,E] def mape(m: Edge[V,E] => T ): Graph[V,T] // Joins def joinv(tbl: Table [(Id, T)]): Graph[(V, T), E ] def joine(tbl: Table [(Id, Id, T)]): Graph[V, (E, T)] // Computation def mrtriplets(mapf: (Edge[V,E]) => List[(Id, T)], reducef: (T, T) => T): Graph[T, E] } 41

42 Graph Operators (Scala) class Graph [ V, E ] { def Graph(vertices: Table[ (Id, V) ], edges: Table[ (Id, Id, E) ]) // Table Views def vertices: Table[ (Id, V) ] def edges: Table[ (Id, Id, E) ] def triplets: Table [ ((Id, V), (Id, V), E) ] // Transformations def reverse: Graph[V, E] def subgraph(pv: (Id, V) => Boolean, pe: Edge[V,E] => Boolean): Graph[V,E] def mapv(m: (Id, V) => T ): Graph[T,E] def mape(m: Edge[V,E] => T ): Graph[V,T] // Joins def joinv(tbl: Table GraphLab [(Id, T)]): Graph[(V, T), E ] def joine(tbl: Table [(Id, Id, T)]): Graph[V, (E, T)] // Computation def mrtriplets(mapf: (Edge[V,E]) => List[(Id, T)], reducef: (T, T) => T): Graph[T, E] } capture the Gather-Scatter pattern from 42

43 Graph System Optimizations Specialized Data-Structures Vertex-Cuts Partitioning Remote Caching / Mirroring Message Combiners Active Set Tracking 43

44 PageRank Benchmark EC2 Cluster of 16 x m2.4xlarge Nodes + 1GigE Runtime (Seconds) Twitter Graph (42M Vertices,1.5B Edges) x UK-Graph (106M Vertices, 3.7B Edges) 18x GraphX performs comparably to state-of-the-art graph processing systems.

45 The GraphX Stack (Lines of Code) PageRank (20) Connected Comp. (20) GraphLab/Pregel API (34) K-core (60) Triangle Count (50) LDA (220) SVD++ (110) GraphX (2,500) Spark (30,000) Some algorithms are more naturally expressed using the GraphX primitive operators

46 Graphs are just one stage. What about a pipeline?

47 A Small Pipeline in GraphX Raw Wikipedia Hyperlinks PageRank Top 20 Pages < / > < / > < / > XML HDFS HDFS Spark Giraph + Spark GraphLab + Spark GraphX Spark Preprocess Compute Spark Post Total Runtime (in Seconds) Timed end-to-end GraphX is the fastest

48 MLBase: Distributed ML Made Easy DB Query Language Analogy: Specify What not How MLBase chooses: Algorithms/Operators Ordering and Physical Placement Parameter and Hyperparameter Settings Featurization Leverages Spark for Speed and Scale T. Kraska, A. Talwalkar, J. Duchi, R. Griffith, M. Franklin, M. Jordan, MLBase: A Distributed Machine Learning System, CIDR 2013.

49 ML Pipeline Generation & Optimization Ex: Image Classification Pipeline MLBase Optimizer Focus: Better Resource Utilization Algorithmic Speedups Reduced Model Search Time E. Sparks, S. Venkataraman, M. Franklin, B. Recht, ML Pipelines, In Process. (see AMPLab Blog for an overview)

50 Introducing Velox: Model Serving Data Training Model Where do models go? Conference Papers Sales Reports Drive Actions 50

51 Driving Actions Suggesting Items at Checkout Low-Latency Fraud Detection Personalized Cognitive Assistance Internet of Things Rapidly Changing 51

52 Problem: Separate Systems Offline Analytics Systems 6. Before Online Serving Systems 7. After Sophisticated ML on static data. 8. After MongoDB Low-Latency data serving How do we serve low-latency predictions and train on live data? 52

53 Velox Model Serving System [CIDR 15] Decompose personalized predictive models: 53

54 Velox Model Serving System [CIDR 15] Decompose personalized predictive models: Feature Caching Feature Model Personalization Model Online Updates Approx. Features Batch Split Online Active Learning Order-of-magnitude reductions in prediction latencies. 54

55 Berkeley Data Analytics Stack (Apache and BSD open source) In-house Apps Access and Interfaces Processing Engine Storage Resource Virtualization

56 Big Data Architecture Open Questions & Research Issues What is the role of a unified stack in a fastchanging software landscape? Single-node vs. Cluster; Elastic Cloud vs HPC GPUs, FPGAs, XYZs,??? New memory hierarchies: SSDs, RDMA, Serving vs. Analytics Workloads What correctness guarantees are really needed?

57 Summary Big Data yes, there s hype but it is a Big Deal AmpLab project 6 Yrs, Cross-disciplinary team, Industry engagement Open Source development and community building BDAS philosophy: Unification Spark + SQL + Graphs + ML + The Big Data landscape continues to evolve Open Source enables Academic research to play a huge role

58 To find out more or get involved: UC BERKELEY amplab.berkeley.edu Thanks to NSF CISE Expeditions in Computing, DARPA XData, Founding Sponsors: Amazon Web Services, Google, and SAP, the Thomas and Stacy Siebel Foundation, all our industrial sponsors and partners, and all the members of the AMPLab Team.